Since nonsmooth optimization problems arise in a diverse range of real-world applications, the potential impact of efficient methods for solving such problems is undeniable. Even solving difficult smooth problems sometimes requires the use of nonsmooth optimization methods, in order to either reduce the problem's scale or simplify its structure. Accordingly, the field of nonsmooth optimization is an important area of mathematical programming that is based on by now classical concepts of variational analysis and generalized derivatives, and has developed a rich and sophisticated set of mathematical tools at the intersection of theory and practice.

This volume of ISNM is an outcome of the workshop "Nonsmooth Optimization and its Applications," which was held from May 15 to 19, 2017 at the Hausdorff Center for Mathematics, University of Bonn. The six research articles gathered here focus on recent results that highlight different aspects of nonsmooth and variational analysis, optimization methods, their convergence theory and applications.

Highlights different aspects of nonsmooth and variational analysis Presents optimization methods and their convergence theory and applications Six research articles gathered focus on recent results

Inhalt
A Collection of Nonsmooth Riemannian Optimization Problems.- An approximate ADMM for solving linearly constrained nonsmooth problems with two blocks of variables.- Tangent and normal cones for low-rank matrices.- Subdifferential enlargements and continuity properties of the VU-decomposition in convex optimization.- Proximal mappings and Moreau envelopes of single-variable convex piecewise cubic functions and multivariable gauge functions.- Newton-like dynamics associated to nonconvex optimization problems.